While AI's technical capabilities advance exponentially, widespread organizational adoption is slowed by human factors like resistance to change, lack of urgency, and abstract understanding. This creates a significant gap between potential and reality.

Even with superhuman AI, Dario Amodei argues the economic revolution won't be instant. The real-world bottleneck is "economic diffusion": the messy, human process of enterprise adoption, including legal reviews, security compliance, and change management, which creates a fast but not infinite adoption curve.

Marc Andreessen contends that AI's potential GDP growth is overestimated because it ignores societal inertia. Sectors like healthcare, education, and unionized labor are protected by licensing and regulations that function as cartels, which will resist and dramatically slow the adoption of new technology.

Despite the power of new AI agents, the primary barrier to adoption is human resistance to changing established workflows. People are comfortable with existing processes, even inefficient ones, making it incredibly difficult for even technologically superior systems to gain traction.

The AI buildout won't be stopped by technological limits or lack of demand. The true barrier will be economics: when the marginal capital provider determines that the diminishing returns from massive investments no longer justify the cost.

Andreessen now largely agrees with Peter Thiel's thesis: technological progress has been confined to "bits" (software) while the world of "atoms" (physical infrastructure, manufacturing) has stagnated for 50 years. This real-world inertia will significantly slow AI's broader economic impact.

Despite rapid software advances like deep learning, the deployment of self-driving cars was a 20-year process because it had to integrate with the mature automotive industry's supply chains, infrastructure, and business models. This serves as a reminder that AI's real-world impact is often constrained by the readiness of the sectors it aims to disrupt.

Economist Tyler Cowen argues AI's productivity boost will be limited because half the US economy—government, nonprofits, higher education, parts of healthcare—is structurally inefficient and slow to adopt new tech. Gains in dynamic sectors are diluted by the sheer weight of these perpetually sluggish parts of the economy.

While AI is capable of disrupting most knowledge work now, large enterprises move too slowly to implement it. Widespread job disruption will be delayed by organizational friction and slow adoption, not technological limitations, even if AGI were achieved today.